LEADER 00910nam a22002531i 4500 001 991003948529707536 005 20230302114948.0 008 040802s1979 be a||||||||||||||||fre 035 $ab13151034-39ule_inst 035 $aARCHE-110364$9ExL 040 $aBibl. Interfacoltà T. Pellegrino$bita$cA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l. 082 04$a794.1 100 1 $aFlesch, János$0490573 245 10$aEchecs :$ble milieu de partie /$cJános Flesch 260 $a[Verviers] :$bMarabout,$cc1979 300 $aVIII, 83 p. :$bill. ;$c22 cm 650 4$aScacchi 830 0$aMarabout service 907 $a.b13151034$b02-04-14$c05-08-04 912 $a991003948529707536 945 $aLE002 Fondo Giudici S 470$g1$i2002000392480$lle002$nC. 1$o-$pE0.00$q-$rn$so$t0$u0$v0$w0$x0$y.i13789223$z05-08-04 996 $aEchecs$9310792 997 $aUNISALENTO 998 $ale002$b05-08-04$cm$da$e-$ffre$gbe$h0$i1 LEADER 05247nam 2200601 450 001 9910820988503321 005 20230807203812.0 010 $a0-8261-9579-2 035 $a(CKB)2550000001328673 035 $a(EBL)1731787 035 $a(SSID)ssj0001262546 035 $a(PQKBManifestationID)12542126 035 $a(PQKBTitleCode)TC0001262546 035 $a(PQKBWorkID)11216837 035 $a(PQKB)10870056 035 $a(MiAaPQ)EBC1731787 035 $a(Au-PeEL)EBL1731787 035 $a(CaPaEBR)ebr10895277 035 $a(CaONFJC)MIL625780 035 $a(OCoLC)883373081 035 $a(EXLCZ)992550000001328673 100 $a20140724h20152015 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aSubstance abuse treatment $eoptions, challenges, and effectiveness /$fSylvia I. Mignon 210 1$aNew York :$cSpringer Publishing Company,$d2015. 210 4$d©2015 215 $a1 online resource (274 p.) 300 $aDescription based upon print version of record. 311 $a0-8261-9578-4 311 $a1-306-94529-1 320 $aIncludes bibliographical references and index. 327 $aCover; Title; Copyright; Contents; Preface; Acknowledgments; Share Substance Abuse Treatment: Options, Challenges, and Effectiveness; Chapter 1: Introduction and Overview: What Is Treatment Effectiveness?; Problems with Definition; Measuring Treatment Effectiveness; What Makes It Hard to Evaluate Treatment Effectiveness?; Acute and Chronic Substance Treatment; Treatment Matching and Evidence-Based Practices; The Research and Clinical Gap; Obstacles to Treatment; Summary and Conclusion; Chapter 2: Treatment Goals: Abstinence and Harm Reduction; Principles of Harm Reduction 327 $aHistorical Perspective Ethical Considerations; Politics of Harm Reduction; The Controlled Drinking Controversy and Moderation Management; Setting Treatment Goals; Summary and Conclusion; Chapter 3: Motivation for Treatment; Definitions of Motivation; Intrinsic Motivation; Extrinsic Motivation; History of Motivation; Stages of Change Model; Epiphanies and Spiritual Awakenings; Interventions by Family; Legally Mandated and Coerced Treatment; The Client and Counselor Relationship; Motivational Interviewing; Summary and Conclusion; Chapter 4: The Substance Abuse Treatment Industry 327 $aThe 1920's Through the 1940's The 1950's and 1960's; The 1970's; The 1980's; The 1990's; The 2000's; Cost-Effectiveness Issues in Substance Abuse Treatment; Summary and Conclusion; Chapter 5: Inpatient Substance Abuse Treatment; Detox Programs; Stabilization Programs; Inpatient Rehabilitation Programs; Residential Programs; Therapeutic Communities; Halfway Houses; Sober Houses; Summary and Conclusion; Chapter 6: Outpatient Substance Abuse Treatment; Project Match; Types or Models of Outpatient Substance Abuse Treatment; Brief Interventions; Outpatient Detoxification; Individual Therapy; Group Therapy 327 $aFamily Therapy or Behavioral Couples Therapy Telephone- and Web-Based Counseling; Pharmacologic Interventions; Methadone Maintenance; Other Pharmacologic Interventions; Counseling Styles and Approaches; Motivational Interviewing; Cognitive Behavioral Therapy; Contingency Management; Community Reinforcement; Adjunct Therapies; Summary and Conclusion; Chapter 7: Self-Help Groups; Research Issues; Alcoholics Anonymous; AA and Personality Traits; Narcotics Anonymous; Al-Anon and Nar-Anon; Self-Help and Treatment Programs Working Together; Limitations of Self-Help Programs; Summary and Conclusion 327 $aChapter 8: Treatment of Diverse Populations Race and Ethnicity; Native Americans; Hispanics and Latinos; African Americans; Asian Americans; Group Comparisons; Adolescents; Women; Abuse History; Pregnant Women; Lesbian, Gay, Bisexual, and Transgender People; Hiv/Aids; Older Adults; Summary and Conclusion; Chapter 9: Treatment of Co-Occurring Disorders (Dual Diagnosis); Social Networks and Family Support; Treatment Issues; Homelessness; Ethnicity; Types of Mental Health Problems; Anxiety Disorders; Depression; Bipolar Disorder; Borderline Personality Disorder; Antisocial Personality Disorder 327 $aAttention Deficit Hyperactivity Disorder 330 $aThe first compendium of all substance abuse treatment options with a focus on best practices. This is the first compendium of the entire range of options available for treating substance abuse, with a focus on effectiveness. The book synthesizes treatment approaches from medicine, psychology, sociology, and social work, and investigates regimens that range from brief interventions to the most intensive and expensive types of inpatient treatment programs. It examines controversies over best practices in substance treatment and closely analyzes current research findings and their applicability 606 $aSubstance abuse$xTreatment 615 0$aSubstance abuse$xTreatment. 676 $a616.86/06 700 $aMignon$b Sylvia I.$01125890 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910820988503321 996 $aSubstance abuse treatment$94050984 997 $aUNINA LEADER 08862nam 2200505 450 001 9910830429503321 005 20240228102704.0 010 $a1-119-60691-8 010 $a1-119-60689-6 010 $a1-119-60692-6 035 $a(CKB)4100000011993129 035 $a(MiAaPQ)EBC6692400 035 $a(Au-PeEL)EBL6692400 035 $a(PPN)276087488 035 $a(OCoLC)1263185565 035 $a(EXLCZ)994100000011993129 100 $a20220423d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEarth observation using Python $ea practical programming guide /$fRebekah Bradley Esmaili 210 1$aHoboken, New Jersey :$cAGU :$cWiley,$d[2021] 210 4$d©2021 215 $a1 online resource (300 pages) 225 1 $aSpecial publications series ;$v75 311 $a1-119-60688-8 327 $aCover -- Title Page -- Copyright Page -- Contents -- Foreword -- Acknowledgments -- Introduction -- Part I Overview of Satellite Datasets -- Chapter 1 A Tour of Current Satellite Missions and Products -- 1.1 History of Computational Scientific Visualization -- 1.2 Brief Catalog of Current Satellite Products -- 1.2.1 Meteorological and Atmospheric Science -- 1.2.2 Hydrology -- 1.2.3 Oceanography and Biogeosciences -- 1.2.4 Cryosphere -- 1.3 The Flow of Data from Satellites to Computer -- 1.4 Learning Using Real Data and Case Studies -- 1.5 Summary -- References -- Chapter 2 Overview of Python -- 2.1 Why Python? -- 2.2 Useful Packages for Remote Sensing Visualization -- 2.2.1 NumPy -- 2.2.2 Pandas -- 2.2.3 Matplotlib -- 2.2.4 netCDF4 and h5py -- 2.2.5 Cartopy -- 2.3 Maturing Packages -- 2.3.1 xarray -- 2.3.2 Dask -- 2.3.3 Iris -- 2.3.4 MetPy -- 2.3.5 cfgrib and eccodes -- 2.4 Summary -- References -- Chapter 3 A Deep Dive into Scientific Data Sets -- 3.1 Storage -- 3.1.1 Single Values -- 3.1.2 Arrays -- 3.2 Data Formats -- 3.2.1 Binary -- 3.2.2 Text -- 3.2.3 Self-Describing Data Formats -- 3.2.4 Table-Driven Formats -- 3.2.5 geoTIFF -- 3.3 Data Usage -- 3.3.1 Processing Levels -- 3.3.2 Product Maturity -- 3.3.3 Quality Control -- 3.3.4 Data Latency -- 3.3.5 Reprocessing -- 3.4 Summary -- References -- Part II Practical Python Tutorials for Remote Sensing -- Chapter 4 Practical Python Syntax -- 4.1 "Hello Earth" in Python -- 4.2 Variable Assignment and Arithmetic -- 4.3 Lists -- 4.4 Importing Packages -- 4.5 Array and Matrix Operations -- 4.6 Time Series Data -- 4.7 Loops -- 4.8 List Comprehensions -- 4.9 Functions -- 4.10 Dictionaries -- 4.11 Summary -- References -- Chapter 5 Importing Standard Earth Science Datasets -- 5.1 Text -- 5.2 NetCDF -- 5.2.1 Manually Creating a Mask Variable Using True and False Values. 327 $a5.2.2 Using NumPy Masked Arrays to Filter Automatically -- 5.3 HDF -- 5.4 GRIB2 -- 5.5 Importing Data Using Xarray -- 5.5.1 netCDF -- 5.5.2 Examining Vertical Cross Sections -- 5.5.3 Examining Horizontal Cross Sections -- 5.5.4 GRIB2 using Cfgrib -- 5.5.5 Accessing Datasets Using OpenDAP -- 5.6 Summary -- References -- Chapter 6 Plotting and Graphs for All -- 6.1 Univariate Plots -- 6.1.1 Histograms -- 6.1.2 Barplots -- 6.2 Two Variable Plots -- 6.2.1 Converting Data to a Time Series -- 6.2.2 Useful Plot Customizations -- 6.2.3 Scatter Plots -- 6.2.4 Line Plots -- 6.2.5 Adding Data to an Existing Plot -- 6.2.6 Plotting Two Side-by-Side Plots -- 6.2.7 Skew-T Log-P -- 6.3 Three Variable Plots -- 6.3.1 Filled Contour Plots -- 6.3.2 Mesh Plots -- 6.4 Summary -- References -- Chapter 7 Creating Effective and Functional Maps -- 7.1 Cartographic Projections -- 7.1.1 Geographic Coordinate Systems -- 7.1.2 Choosing a Projection -- 7.1.3 Some Common Projections -- 7.2 Cylindrical Maps -- 7.2.1 Global Plots -- 7.2.2 Changing Projections -- 7.2.3 Regional Plots -- 7.2.4 Swath Data -- 7.2.5 Quality Flag Filtering -- 7.3 Polar Stereographic Maps -- 7.4 Geostationary Maps -- 7.5 Creating Maps from Datasets Using OpenDAP -- 7.6 Summary -- References -- Chapter 8 Gridding Operations -- 8.1 Regular One-Dimensional Grids -- 8.2 Regular Two-Dimensional Grids -- 8.3 Irregular Two-Dimensional Grids -- 8.3.1 Resizing -- 8.3.2 Regridding -- 8.3.3 Resampling -- 8.4 Summary -- References -- Chapter 9 Meaningful Visuals through Data Combination -- 9.1 Spectral and Spatial Characteristics of Different Sensors -- 9.2 Normalized Difference Vegetation Index (NDVI) -- 9.3 Window Channels -- 9.4 RGB -- 9.4.1 True Color -- 9.4.2 Dust RGB -- 9.4.3. Fire/Natural RGB -- 9.5 Matching with Surface Observations -- 9.5.1 With User-Defined Functions -- 9.5.2 With Machine Learning. 327 $a9.6 Summary -- References -- Chapter 10 Exporting with Ease -- 10.1 Figures -- 10.2 Text Files -- 10.3 Pickling -- 10.4 NumPy Binary Files -- 10.5 NetCDF -- 10.5.1 Using netCDF4 to Create netCDF Files -- 10.5.2 Using Xarray to Create netCDF Files -- 10.5.3 Following Climate and Forecast (CF) Metadata Conventions -- 10.6 Summary -- Part III Effective Coding Practices -- Chapter 11 Developing a Workflow -- 11.1 Scripting with Python -- 11.1.1 Creating Scripts Using Text Editors -- 11.1.2 Creating Scripts from Jupyter Notebook -- 11.1.3 Running Python Scripts from the Command Line -- 11.1.4 Handling Output When Scripting -- 11.2 Version Control -- 11.2.1 Code Sharing though Online Repositories -- 11.2.2 Setting up on GitHub -- 11.3 Virtual Environments -- 11.3.1 Creating an Environment -- 11.3.2 Changing Environments from the Command Line -- 11.3.3 Changing Environments in Jupyter Notebook -- 11.4 Methods for Code Development -- 11.5 Summary -- References -- Chapter 12 Reproducible and Shareable Science -- 12.1 Clean Coding Techniques -- 12.1.1 Stylistic Conventions -- 12.1.2 Tools for Clean Code -- 12.2 Documentation -- 12.2.1 Comments and Docstrings -- 12.2.2 README File -- 12.2.3 Creating Useful Commit Messages -- 12.3 Licensing -- 12.4 Effective Visuals -- 12.4.1 Make a Statement -- 12.4.2 Undergo Revision -- 12.4.3 Are Accessible and Ethical -- 12.5 Summary -- References -- Conclusion -- Appendix A Installing Python -- A.1. Download Tutorials for This Book -- A.2. Download and Install Anaconda -- A.3. Package Management in Anaconda -- Appendix B Jupyter Notebook -- B.1. Running on a Local Machine (New Coders) -- B.2. Running on a Remote Server (Advanced) -- B.3. Tips for Advanced Users -- B.3.1. Customizing Notebooks with Configuration Files -- B.3.2. Starting and Ending Python Scripts -- B.3.3. Creating Git Commit Templates. 327 $aAppendix C Additional Learning Resources -- Appendix D Tools -- D.1. Text Editors and IDEs -- D.2. Terminals -- Appendix E Finding, Accessing, and Downloading Satellite Datasets -- E.1. Ordering Data from NASA EarthData -- E.2. Ordering Data from NOAA/CLASS -- Appendix F Acronyms -- Index -- EULA. 330 $a"Python is a modern programming language that has exploded in popularity both inside and outside of the Earth science community. Part of its appeal is it's easy-to-learn syntax and the thousands of available libraries which can be synthesized with core Python to do nearly any computing task imaginable. In particular, Python is useful for reading Earth-observing satellite datasets, which can be notoriously difficult to use due to the volume of information that results from the multitude of sensors, platforms, and spatio-temporal spacing. Python facilitates reading a variety of self-describing binary datasets that these observations are often encoded in. Using the same software, one can complete the entirerty of a research project and even produce plots. Within a notebook environment, the scientist can document and distribute the code which can improve efficiency and transparency within the Earth sciences community. Even with the right tools data are seldom ready off-the-shelf for analysis and research and requires a number of pre-processing steps to make the data useable. What steps to take and why are often except perhaps for data developers themselves. Data users often misunderstand concepts such as data quality, how to perform an atmospheric correction, or the complex regridding schemes necessary to compare data with different resolutions. Even to a technical user, the nuances can be frustrating and difficult to overcome. The consequence of this is that data remains unused, or worse, potentially misused"--$cProvided by publisher. 410 0$aSpecial publication (American Geophysical Union) ;$v75. 606 $aEarth sciences$xData processing 615 0$aEarth sciences$xData processing. 676 $a550.2855133 700 $aEsmaili$b Rebekah Bradley$01647677 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830429503321 996 $aEarth observation using Python$93995396 997 $aUNINA LEADER 02770nam 22006974a 450 001 9910962047803321 005 20200520144314.0 010 $a9786610274970 010 $a9781118856475 010 $a1118856473 010 $a9781280274978 010 $a1280274972 010 $a9780470020487 010 $a0470020482 035 $a(CKB)1000000000020112 035 $a(EBL)210551 035 $a(OCoLC)475919035 035 $a(SSID)ssj0000155000 035 $a(PQKBManifestationID)11158444 035 $a(PQKBTitleCode)TC0000155000 035 $a(PQKBWorkID)10098316 035 $a(PQKB)11021484 035 $a(MiAaPQ)EBC210551 035 $a(Au-PeEL)EBL210551 035 $a(CaPaEBR)ebr10113956 035 $a(CaONFJC)MIL27497 035 $a(OCoLC)62790653 035 $a(Perlego)1009750 035 $a(EXLCZ)991000000000020112 100 $a20040416d2004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aFinancial instrument pricing using C++ /$fDaniel J Duffy 205 $a1st ed. 210 $aHoboken, NJ $cJohn Wiley$dc2004 215 $a1 online resource (434 p.) 225 1 $aThe Wiley Finance Series 300 $aIncludes bibliographical references (p. [397]-399) and index. 311 08$a9780470855096 311 08$a0470855096 327 $aTemplate programming in C++ -- Building block classes -- Ordinary and stochastic differential equations -- Programming the black-scholes environment -- Design patterns -- Design and deployment issues. 330 $aOne of the best languages for the development of financial engineering and instrument pricing applications is C++. This book has several features that allow developers to write robust, flexible and extensible software systems. The book is an ANSI/ISO standard, fully object-oriented and interfaces with many third-party applications. It has support for templates and generic programming, massive reusability using templates (?write once?) and support for legacy C applications. In this book, author Daniel J. Duffy brings C++ to the next level by applying it to the design and implementation of cla 410 0$aWiley finance series. 606 $aInvestments$xMathematical models 606 $aFinancial engineering 606 $aC++ (Computer program language) 615 0$aInvestments$xMathematical models. 615 0$aFinancial engineering. 615 0$aC++ (Computer program language) 676 $a332.6/0285/5133 700 $aDuffy$b Daniel J$0103056 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910962047803321 996 $aFinancial instrument pricing using C++$93952486 997 $aUNINA